integrate ROUGE

examples/summarization/modeling_bertabs.py

@@ -21,9 +21,6 @@
 # SOFTWARE.
 import copy
 import math
-import shutil
-import time
-import os
 
 import numpy as np
 import torch
@@ -1082,11 +1079,6 @@ class Translator(object):
 
         return translations
 
-    def _report_rouge(self, gold_path, can_path):
-        self.logger.info("Calculating Rouge")
-        results_dict = test_rouge(self.args.temp_dir, can_path, gold_path)
-        return results_dict
-
 
 def tile(x, count, dim=0):
     """
@@ -1113,63 +1105,10 @@ def tile(x, count, dim=0):
 
 
 #
-# All things ROUGE. Uses `pyrouge` which is a hot mess.
+# Optimizer for training. We keep this here in case we want to add
+# a finetuning script.
 #
 
-
-def test_rouge(temp_dir, cand, ref):
-    candidates = [line.strip() for line in open(cand, encoding="utf-8")]
-    references = [line.strip() for line in open(ref, encoding="utf-8")]
-    print(len(candidates))
-    print(len(references))
-    assert len(candidates) == len(references)
-
-    cnt = len(candidates)
-    current_time = time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime())
-    tmp_dir = os.path.join(temp_dir, "rouge-tmp-{}".format(current_time))
-    if not os.path.isdir(tmp_dir):
-        os.mkdir(tmp_dir)
-        os.mkdir(tmp_dir + "/candidate")
-        os.mkdir(tmp_dir + "/reference")
-    try:
-
-        for i in range(cnt):
-            if len(references[i]) < 1:
-                continue
-            with open(
-                tmp_dir + "/candidate/cand.{}.txt".format(i), "w", encoding="utf-8"
-            ) as f:
-                f.write(candidates[i])
-            with open(
-                tmp_dir + "/reference/ref.{}.txt".format(i), "w", encoding="utf-8"
-            ) as f:
-                f.write(references[i])
-        r = pyrouge.Rouge155(temp_dir=temp_dir)
-        r.model_dir = tmp_dir + "/reference/"
-        r.system_dir = tmp_dir + "/candidate/"
-        r.model_filename_pattern = "ref.#ID#.txt"
-        r.system_filename_pattern = r"cand.(\d+).txt"
-        rouge_results = r.convert_and_evaluate()
-        print(rouge_results)
-        results_dict = r.output_to_dict(rouge_results)
-    finally:
-        pass
-        if os.path.isdir(tmp_dir):
-            shutil.rmtree(tmp_dir)
-    return results_dict
-
-
-def rouge_results_to_str(results_dict):
-    return ">> ROUGE-F(1/2/3/l): {:.2f}/{:.2f}/{:.2f}\nROUGE-R(1/2/3/l): {:.2f}/{:.2f}/{:.2f}\n".format(
-        results_dict["rouge_1_f_score"] * 100,
-        results_dict["rouge_2_f_score"] * 100,
-        results_dict["rouge_l_f_score"] * 100,
-        results_dict["rouge_1_recall"] * 100,
-        results_dict["rouge_2_recall"] * 100,
-        results_dict["rouge_l_recall"] * 100,
-    )
-
-
 class BertSumOptimizer(object):
     """ Specific optimizer for BertSum.